Christine M. Wickens

Cognitive failures as predictors of driving errors, lapses, and violations

Dual-process models from the cognitive literature have proposed a taxonomy of cognitive failures in everyday activities, and this novel approach was applied to understanding driver behaviour. This framework was used to examine whether categories of cognitive failure would explain driving errors, driving lapses, and driving violations in a sample of undergraduates at a large urban university. Two types of cognitive failure were examined, one associated with missing affective information and the other associated with a failure to engage effortful processes to override an automatic response. Alexithymia was used as an indicator of missing affective information, and attention regulation, reactivity, and impulsivity were used as indicators of override failure. Relevant demographic variables included gender and hours typically driven. Override failures were significantly associated with driving behaviour in the correlational analyses. In the regression analyses, attention regulation predicted driving errors, and gender, attention regulation, and impulsivity predicted driving violations. The implications of this work include the potential application to driver training, to users of informatics devices (e.g., GPS, cellular phones, messaging systems), and for individuals diagnosed with attention and/or impulsivity problems.

Understanding driver anger and aggression: Attributional theory in the driving environment

Two studies tested the applicability of Weiners (1995, 1996, 2001, 2006) attributional model of social conduct to roadway environments. This model highlights the role of inferences of responsibility after making causal judgments for social transgressions. Study 1 employed written scenarios where participants were asked to imagine themselves driving on a major highway. The degree of controllability and intentionality of the driving act was manipulated experimentally by altering the specific event-related details provided to the participants. Study 2 extended this research to life events by having participants complete online driving diaries every 2 days, identifying their most negative/upsetting encounter with another motorist. The most anger-provoking event was selected from among 4 diary entries and participants were asked to respond to a questionnaire similar to that used in Study 1. Path analyses in both studies generally supported predictions derived from Weiners model; the association between perceived controllability, intentionality, and dispositional locus of causality of the negative driving event and subsequent anger was mediated by perceptions of responsibility. Additional results in Study 2 suggested that low perceived controllability, intentionality, and dispositional locus of causality were associated with reduced perceived responsibility, which, in turn, facilitated feelings of sympathy. Anger was associated with aggressive responses to the offending driver, whereas sympathy was associated with prosocial responses. Recommendations were offered for improved driver safety, including the development of attributional retraining programs to combat self-serving attributional biases, teaching novice drivers about both formal and informal roadway communication, and the promotion of forgiveness among drivers

Alcohol and driving factors in collision risk.

In this study we examine the effect of several alcohol-related measures on self-reported collision involvement within the previous 12 months while controlling for demographic and driving exposure factors based on a large representative sample of adults in Ontario. Data are based on the 2002-2006 Centre for Addiction and Mental Health Monitor, an ongoing cross-sectional telephone survey of Ontario adults aged 18 and older (n=8542). Three logistic regressions of self-reported collision involvement in the past 12 months were implemented, each consisting of 3 steps: (1) demographic factors and driving exposure entered, (2) driving after drinking within the last 12 months entered, and (3) one of three alcohol-related measures (AUDIT subscales of alcohol consumption, dependence and problems) entered. In each step, measures from the preceding step were included in order to control for those variables. In Step 1, age (OR=0.989), region overall, Central East region (OR=0.71), West region (OR=0.67), and North region (OR=0.67), income overall and those who did not state income (OR=0.64), marital status overall and those married or living common law (OR=0.60), and number of kilometers driven in a typical week (OR=1.00) were found to be significant predictors of collision involvement. The analyses revealed that driving after drinking was a significant predictor of collision involvement in Step 2 (OR=1.51) and each of the Step 3 models (ORs=1.52, 1.37, 1.34). The AUDIT Consumption subscale was not a significant factor in collision risk. Both the AUDIT Dependence and AUDIT Problems subscales were significantly related to collision risk (ORs=1.13 and 1.10, respectively). These findings suggest that alcohol, in addition to its effects on collision risk through its acute impairment of driving skills, may also affect collision risk through processes involved when individuals develop alcohol problems or alcohol dependence.

Self-Reported Collision Risk Associated With Cannabis Use and Driving After Cannabis Use Among Ontario Adults

Objective: This study examined the effects of cannabis use and driving after cannabis use on self-reported collision involvement within the previous 12 months while controlling for demographics, driving exposure, binge drinking, and driving after drinking based on a large representative sample of adults in Ontario. Methods: Data are based on the CAMH Monitor, an ongoing cross-sectional telephone survey of Ontario adults aged 18 and older, conducted by the Centre for Addiction and Mental Health. Data on drivers who reported driving at least one kilometer per week and who responded to the collision item from 2002 to 2007 were merged into one data set (n = 8481). Logistic regression analysis of self-reported collision risk posed by cannabis use (lifetime and past 12 months), driving after cannabis use (past 12 months), and driving after drinking among drinkers (past 12 months) was implemented, controlling for the effects of gender, age, region, income, education, marital status, kilometers driven in a typical week, and consuming five or more drinks of alcohol on one occasion (past 12 months). Due to list-wise deletion of cases the logistic regression sample was reduced (n = 6907). Results: Several demographic factors were found to be significantly associated with self-reported collision involvement. The logistic regression model revealed that age, region, income, marital status, and number of kilometers driven in a typical week, were all significantly related to collision involvement, after adjusting for other factors. Respondents who reported having driven after cannabis use within the past 12 months had increased risk of collision involvement (odds ratio [OR] = 1.84) compared to those who never drove after using cannabis, a greater risk than that associated with having reported driving after drinking within the past 12 months (OR = 1.34). Conclusion: Further investigation of the impact of driving after cannabis use on collision risk and factors that may modify that relationship is warranted.

Does gender moderate the relationship between driver aggression and its risk factors

AIM The current study assessed gender as a potential moderator of the relationship between self-reported driver aggression and various demographic variables, general and driving-related risk factors. METHODS Using data from a general-population telephone survey conducted from July 2002 through June 2005, two approaches to binary logistic regression were adopted. Based on the full dataset (n=6259), the initial analysis was a hierarchical-entry regression examining self-reported driver aggression in the last 12 months. All demographic variables (i.e., gender, age, income, education, marital status), general risk factors (i.e., psychological distress, binge drinking, cannabis use), and driving-related risk factors (i.e., driving exposure, stressful driving, exposure to busy roads, driving after drinking, driving after cannabis use) were entered in the first block, and all two-way interactions with gender were entered stepwise in the second block. The subsequent analysis involved dividing the sample by gender and conducting logistic regressions with main effects only for males (n=2921) and females (n=3338) separately. RESULTS Although the prevalence of driver aggression in the current sample was slightly higher among males (38.5%) than females (32.9%), the difference was small, and gender did not enter as a significant predictor of driver aggression in the overall logistic regression. In that analysis, difficulty with social functioning and being older were associated with a reduced risk of driver aggression. Marital status and education were unrelated to aggression, and all other variables were associated with an increased risk of aggression. Gender was found to moderate the relationships between driver aggression and only three variables: income, psychological distress, and driving exposure. Separate analyses on the male and female sub-samples also found differences in the predictive value of income and driving exposure; however, the difference for psychological distress could not be detected using this separate regression approach. The secondary analysis also identified slight differences in the predictive value of four of the risk factors, where the odds ratios for both males and females were in the same direction but only one of the two was statistically significant. CONCLUSIONS The results demonstrate the importance of conducting the gender analysis using both regression approaches. With few exceptions, factors that were predictive of driver aggression were generally the same for both male and female drivers.

Addressing Driver Aggression Contributions From Psychological Science

Aggressive roadway behavior contributes to motor-vehicle collisions, resulting in significant injuries, fatalities, and related financial costs. Psychological models have identified person- and situation-related variables that are predictive of driver aggression, and these have been used to develop strategies to alleviate aggressive roadway behavior. Future psychological research directions are discussed.

Driver anger on the information superhighway: A content analysis of online complaints of offensive driver behaviour

In recent years, several websites have been developed allowing drivers to post their complaints about other motorists online. These websites allow drivers to describe the nature of the offensive behaviour and to identify the offending motorist by vehicle type, colour, and license plate number. Some websites also ask drivers to list the location where the event took place and the exact date and time of the offence. The current study was a content analysis of complaints posted to RoadRagers.com between 1999 and 2007 (N=5624). The purpose of the study was to: (1) assess the research value of this novel data source; (2) demonstrate the value of content analysis to the study of driver behaviour; (3) further validate an existing coding scheme; (4) determine whether this new data source would replicate previous research findings regarding the most frequent types of driver complaints and temporal distribution of these reports; (5) provide recommendations for improved driver training and public safety initiatives based on these data. A coding scheme that was originally developed for an assessment of complaints submitted to the Ontario Provincial Police (OPP) (Wickens et al., 2005) was revised to accommodate the new dataset. The inter-rater reliability of the revised coding scheme as applied to the website complaints was very good (kappa=.85). The most frequently reported improper driver behaviours were cutting/weaving, speeding, perceived displays of hostility, and tailgating. Reports were most frequent on weekdays and during the morning and afternoon rush hour. The current study replicated several findings from the analysis of reports to the OPP, but possible differences in the sample and data collection method also produced some differences in findings. The value of content analysis to driver behaviour research and of driver complaint websites as a data source was demonstrated. Implications for driver safety initiatives and future research will be discussed.

The impact of probable anxiety and mood disorder on self-reported collisions: A population study

BACKGROUND Individuals diagnosed with psychiatric disorder are at significantly increased risk of death and serious injury, to which motor vehicle collisions may be important contributors. This study examined the association between probable anxiety or mood disorder (AMD) and self-reported collision risk in a large representative sample of the adult population in Ontario. METHODS Based on data from a regionally stratified general-population telephone survey of adults conducted from 2002 through 2009 (N=12,830), a logistic regression analysis examined self-reported collision involvement in the previous 12 months by measures of demographic characteristics, driving exposure, impaired driving behaviour, and probable AMD. RESULTS Controlling for demographic variables and potential confounders, probable AMD was associated with an increased risk of collision involvement (OR=1.78, 95% CI=1.37, 2.31). LIMITATIONS The use of self-report measures and the potential for bias created by groups excluded because they do not have access to landline telephones represent limitations to the current findings. Nevertheless, the benefits of a large sample derived from general population survey data far outweigh these limitations. CONCLUSIONS The results suggest that the increased risk of injury and mortality associated with some psychiatric disorders is at least partially related to increased risk of collision involvement. The magnitude of the increase in risk associated with probable AMD is similar to that seen among individuals who drive after drinking or using cannabis. In view of these findings, more work to understand this risk among individuals experiencing probable AMD and how it can be avoided is necessary.

Gender Differences and Demographic influences in Perceived Concern for Driver Safety and Support for Impaired Driving Countermeasures

INTRODUCTION Driving safety, impaired driving, and legislation to address these concerns remain important issues. It is imperative countermeasures be targeted toward the most appropriate groups. This paper explores the potential relationship between gender and driving attitudes toward safety issues and impaired-driving countermeasures. METHOD The data are from the 2007 Impaired Driving Survey commissioned by Transport Canada and Mothers Against Drunk Driving (MADD) Canada. The survey is a, stratified by region, telephone survey of 1,514 Canadian drivers 18years of age and older with a valid drivers license who had driven within the past 30days. RESULTS The findings illustrate a consistent impact of gender on these issues. Other variables were also identified as relevant factors although less consistently. Current findings suggest that strategies for building support for interventions, or for changing risk perception/concern for risky driving behaviors should be tailored by gender to maximize the potential for behavior change. IMPACT This information may assist program and policy developers through the identification of more or less receptive target groups. Future research directions are also presented.

Estimating prevalence of anxiety and mood disorder in survey data using the GHQ12: Exploration of threshold values

Background and Objectives: Our study explored the validity of different threshold values on the 12-item version of the General Health Questionnaire (GHQ12) for estimating the prevalence of anxiety and mood disorders (AMD) in Ontario population survey data. a Funding and support: This research has been supported by funding from the Ontario Ministry of Health and Long-Term Care. The views expressed here do not necessarily reflect those of the Ministry. 82 ROBERT E. MANN ET AL.